Network embedding is to learn low-dimensional vector representations for nodes in a network. It has shown to be effective in a variety of tasks such as node classification and link prediction. While embedding algorithms on pure networks have been intensively studied, in many real-world applications, nodes are often accompanied with a rich set of attributes or features, aka attributed networks. ...

it has been proved that sphericity testing for digraphs is an np-complete problem. here, we investigate sphericity of 3-connected single source digraphs. we provide a new combinatorial characterization of sphericity and give a linear time algorithm for sphericity testing. our algorithm tests whether a 3-connected single source digraph with $n$ vertices is spherical in $o(n)$ time.

In network analysis, a community is typically considered of as a group of nodes with a great density of edges among themselves and a low density of edges relative to other network parts. Detecting a community structure is important in any network analysis task, especially for revealing patterns between specified nodes. There is a variety of approaches presented in the literature for overlapping...

Network embedding is a highly effective method to learn low-dimensional node vector representations with original network structures being well preserved. However, existing algorithms are mostly developed for single network, which fail generalized feature across different networks. In this paper, we study cross-network classification problem, aims at leveraging the abundant labeled information ...

This paper presents an efficient algorithm for matching subgraph queries in a multi-graph based on features-based indexing techniques. The KD-tree data structure represents these nodes’ features, while the set-trie index multi-edges to make effectively. vertex core number, triangle and degree are eight features’ main features. densest query graph is extracted proposed model consists of two phas...

Network representation learning (NRL) advances the conventional graph mining of social networks, knowledge graphs, and complex biomedical physics information networks. Dozens NRL algorithms have been reported in literature. Most them focus on node embeddings for homogeneous but they differ specific encoding schemes types semantics captured used embedding. This article reviews design principles ...

Geodesic convexity in networks is an intrinsic property of graphs. It aids distinguishing between real-world and random One possible application recommending new connections a collaborative network by searching for them the so-called convex hull, which minimal subgraph containing all shortest paths its nodes. However, existing algorithms constructing hulls from subsets nodes involve extensive s...